The present invention relates in general to monitoring a location of a roadway lane relative to a vehicle, and, more specifically, to improved lane detection during times when optical identifiers are not present.
Automatic lane detection and monitoring is useful for supporting various driver assistance systems such as a lane departure warning system or a lane keeping assist system. The primary sensor being used in conventional lane detection systems is vision-based, e.g., an optical camera. A lane detection algorithm detects lane markings such as painted lane lines or surface features corresponding to a road edge, and then estimates a vehicle's lateral position within the lane, the lane width, and the vehicle's heading angle with respect to the lane.
Currently used image processing technologies in lane detection algorithms have sufficiently advanced to detect various kinds of lane markings and road edges in a wide range of conditions. However, the lane markings on road surfaces can still be hard to detect. They may be worn away or covered by dirt. There are many other possible impediments that cause the optical system to fail to detect the lane location, such as shadows, overhead bridges, rain, and snow. In such cases, gaps may form in the representation of the lane being tracked. When the lane is lost by the optical system, the lane departure warning/lane keeping assist system is disabled so that no action is taken based on inaccurate or missing information.
It would be desirable to estimate or fill-in any missing lane markings in order to improve overall system availability. When multiple lane borders are being tracked and the markings for one border temporarily disappear, it is known to reconstruct the missing border at a fixed offset distance from the detected border. Nevertheless, instances still occur when the camera-based system is unable to produce a valid output.
Another possibility for lane tracking is through the use of geopositioning to pinpoint a vehicle location and correlate that location onto a digital map representing the roadway. Geographic coordinates are typically measured with an on-board GPS receiving-unit and/or a dead-reckoning system based on inertial sensor outputs in the is vehicle. In addition to the current position, these systems can also provide an instantaneous vehicle speed and heading angle.
Map databases have been constructed for the most of the roads in the country, making it theoretically possible to determine lane placement of the vehicle. Geometric and attribute information about the roadway at the matching coordinates for the vehicle can be looked up from the digital map database. The collection of this road information around a vehicle is called an Electronic Horizon (EH). In a typical EH system, roadways are composed of many road segments (also called links) for which the road geometric and attribute information is defined. The geometric information of a road segment includes the longitude, latitude, elevation, horizontal curvature, and grade along the road. The road attribute information may include road signs, number of lanes, road function class (e.g., freeway, ramp, arterial), lane marking type, paved/unpaved, and divided/undivided.
Although the number of lanes may be represented, the map database typically does not directly represent the coordinates of individual lanes because of the significant increase in the volume of data that would have to be represented. Instead, the links represent a one-dimensional pathline that typically corresponds with the centerline of the roadway. Even in the event that a digital map database does directly represent actual lane boundaries for a given roadway, issues of sporadic positional errors and intermittent availability of the geopositioning systems have limited the reliability of these systems. Consequently, optical camera-based lane monitoring systems have usually been preferred over GPS-based.